The lung, a vital organ in the human respiratory system, plays a critical role in regulating blood oxygen levels through gas exchange. However, with rising pollution levels, lung-related diseases are becoming increasingly prevalent. Early detection of such disorders is essential for safeguarding human health and improving treatment outcomes. Currently, medical professionals rely on imaging techniques such as CT scans and X-rays to detect lung diseases, using their clinical expertise to interpret the results. However, this manual diagnostic process is subject to variability in experience and knowledge, which can lead to diagnostic errors. Additionally, identifying lung conditions in their early stages can be difficult, and in many cases, diagnosis may occur too late for effective intervention. To address these challenges and enhance the accuracy of disease detection, there is a growing need for automated computer algorithms that can function independently of human input. By enabling earlier and more reliable diagnoses, such systems could significantly contribute to life-saving medical interventions. Machine Learning (ML) offers a promising solution in this regard. Through the development or enhancement of ML algorithms, medical researchers aim to predict and diagnose lung diseases more accurately and at earlier stages. To validate the effectiveness of the proposed system, extensive testing will be conducted using a diverse set of medical images. Key performance metrics—such as training accuracy, testing accuracy, training time, and algorithm robustness—will be evaluated to assess the system’s reliability and efficiency.